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Author: Yanjun Huang
Publisher: Springer Nature
ISBN: 3031014979
Category : Technology & Engineering
Languages : en
Pages : 84
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Book Description
As public attention on energy conservation and emission reduction has increased in recent years, engine idling has become a growing concern due to its low efficiency and high emissions. Service vehicles equipped with auxiliary systems, such as refrigeration, air conditioning, PCs, and electronics, usually have to idle to power them. The number of service vehicles (e.g. public-school-tour buses, delivery-refrigerator trucks, police cars, ambulances, armed vehicles, firefighter vehicles) is increasing significantly with tremendous social development. Therefore, introducing new anti-idling solutions is inevitably vital for controlling energy unsustainability and poor air quality. There are a few books about the idling disadvantages and anti-idling solutions. Most of them are more concerned with different anti-idling technologies and their effects on the society rather than elaborating an anti-idling system design considering different applications and limitations. There is still much room to improve existing anti-idling technologies and products. In this book, we took a service vehicle, refrigerator truck, as an example to demonstrate the whole process of designing, optimizing, controlling, and developing a smart charging system for the anti-idling purpose. The proposed system cannot only electrify the auxiliary systems to achieve anti-idling, but also utilize the concepts of regenerative braking and optimal charging strategy to arrive at an optimum solution. Necessary tools, algorithms, and methods are illustrated and the benefits of the optimal anti-idling solution are evaluated.
Author: Yanjun Huang
Publisher: Springer Nature
ISBN: 3031014979
Category : Technology & Engineering
Languages : en
Pages : 84
Get Book Here
Book Description
As public attention on energy conservation and emission reduction has increased in recent years, engine idling has become a growing concern due to its low efficiency and high emissions. Service vehicles equipped with auxiliary systems, such as refrigeration, air conditioning, PCs, and electronics, usually have to idle to power them. The number of service vehicles (e.g. public-school-tour buses, delivery-refrigerator trucks, police cars, ambulances, armed vehicles, firefighter vehicles) is increasing significantly with tremendous social development. Therefore, introducing new anti-idling solutions is inevitably vital for controlling energy unsustainability and poor air quality. There are a few books about the idling disadvantages and anti-idling solutions. Most of them are more concerned with different anti-idling technologies and their effects on the society rather than elaborating an anti-idling system design considering different applications and limitations. There is still much room to improve existing anti-idling technologies and products. In this book, we took a service vehicle, refrigerator truck, as an example to demonstrate the whole process of designing, optimizing, controlling, and developing a smart charging system for the anti-idling purpose. The proposed system cannot only electrify the auxiliary systems to achieve anti-idling, but also utilize the concepts of regenerative braking and optimal charging strategy to arrive at an optimum solution. Necessary tools, algorithms, and methods are illustrated and the benefits of the optimal anti-idling solution are evaluated.
Author: Chen Lv
Publisher: Springer Nature
ISBN: 3031015045
Category : Technology & Engineering
Languages : en
Pages : 78
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Book Description
This book studies the design optimization, state estimation, and advanced control methods for cyber-physical vehicle systems (CPVS) and their applications in real-world automotive systems. First, in Chapter 1, key challenges and state-of-the-art of vehicle design and control in the context of cyber-physical systems are introduced. In Chapter 2, a cyber-physical system (CPS) based framework is proposed for high-level co-design optimization of the plant and controller parameters for CPVS, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. In Chapter 3, an Artificial-Neural-Network-based estimation method is studied for accurate state estimation of CPVS. In Chapter 4, a high-precision controller is designed for a safety-critical CPVS. The detailed control synthesis and experimental validation are presented. The application results presented throughout the book validate the feasibility and effectiveness of the proposed theoretical methods of design, estimation, control, and optimization for cyber-physical vehicle systems.
Author: Haotian Cao
Publisher: Springer Nature
ISBN: 3031015061
Category : Technology & Engineering
Languages : en
Pages : 128
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Book Description
The intelligent vehicle will play a crucial and essential role in the development of the future intelligent transportation system, which is developing toward the connected driving environment, ultimate driving safety, and comforts, as well as green efficiency. While the decision making, planning, and control are extremely vital components of the intelligent vehicle, these modules act as a bridge, connecting the subsystem of the environmental perception and the bottom-level control execution of the vehicle as well. This short book covers various strategies of designing the decision making, trajectory planning, and tracking control, as well as share driving, of the human-automation to adapt to different levels of the automated driving system. More specifically, we introduce an end-to-end decision-making module based on the deep Q-learning, and improved path-planning methods based on artificial potentials and elastic bands which are designed for obstacle avoidance. Then, the optimal method based on the convex optimization and the natural cubic spline is presented. As for the speed planning, planning methods based on the multi-object optimization and high-order polynomials, and a method with convex optimization and natural cubic splines, are proposed for the non-vehicle-following scenario (e.g., free driving, lane change, obstacle avoidance), while the planning method based on vehicle-following kinematics and the model predictive control (MPC) is adopted for the car-following scenario. We introduce two robust tracking methods for the trajectory following. The first one, based on nonlinear vehicle longitudinal or path-preview dynamic systems, utilizes the adaptive sliding mode control (SMC) law which can compensate for uncertainties to follow the speed or path profiles. The second one is based on the five-degrees-of-freedom nonlinear vehicle dynamical system that utilizes the linearized time-varying MPC to track the speed and path profile simultaneously. Toward human-automation cooperative driving systems, we introduce two control strategies to address the control authority and conflict management problems between the human driver and the automated driving systems. Driving safety field and game theory are utilized to propose a game-based strategy, which is used to deal with path conflicts during obstacle avoidance. Driver's driving intention, situation assessment, and performance index are employed for the development of the fuzzy-based strategy. Multiple case studies and demos are included in each chapter to show the effectiveness of the proposed approach. We sincerely hope the contents of this short book provide certain theoretical guidance and technical supports for the development of intelligent vehicle technology.
Author: Teng Liu
Publisher: Springer Nature
ISBN: 3031015037
Category : Technology & Engineering
Languages : en
Pages : 90
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Book Description
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Author: Xiaolin Song
Publisher: Springer Nature
ISBN: 3031015096
Category : Technology & Engineering
Languages : en
Pages : 160
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Book Description
A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Author: Yeuching Li
Publisher: Morgan & Claypool Publishers
ISBN: 1636393020
Category : Computers
Languages : en
Pages : 135
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Book Description
The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.
Author: Sampo Kuutti
Publisher: Springer Nature
ISBN: 3031015029
Category : Technology & Engineering
Languages : en
Pages : 70
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Book Description
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
Author: Xiaolin Tang
Publisher: Springer Nature
ISBN: 3031014987
Category : Technology & Engineering
Languages : en
Pages : 126
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Book Description
Thanks to the potential of reducing fuel consumption and emissions, hybrid electric vehicles (HEVs) have been attracting more and more attention from car manufacturers and researchers. Due to involving two energy sources, i.e., engine and battery, the powertrain in HEVs is a complicated electromechanical coupling system that generates noise and vibration different from that of a traditional vehicle. Accordingly, it is very important to explore the noise and vibration characteristics of HEVs. In this book, a hybrid vehicle with two motors is taken as an example, consisting of a compound planetary gear set (CPGS) as the power-split device, to analyze the noise and vibration characteristics. It is specifically intended for graduates and anyone with an interest in the electrification of full hybrid vehicles. The book begins with the research background and significance of the HEV. The second chapter presents the structural description and working principal of the target hybrid vehicle. Chapter 3 highlights the noise, vibration, and harshness (NVH) tests and corresponding analysis of the hybrid powertrain. Chapter 4 provides transmission system parameters and meshing stiffness calculation. Chapter 5 discusses the mathematical modeling and analyzes torsional vibration (TV) of HEVs. Finally, modeling of the hybrid powertrain with ADAMS is given in Chapter 6.
Author: Yechen Qin
Publisher: Springer Nature
ISBN: 3031014995
Category : Technology & Engineering
Languages : en
Pages : 138
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Book Description
Ever stringent vehicle safety legislation and consumer expectations inspire the improvement of vehicle dynamic performance, which result in a rising number of control strategies for vehicle dynamics that rely on driving conditions. Road profiles, as the primary excitation source of vehicle systems, play a critical role in vehicle dynamics and also in public transportation. Knowledge of precise road conditions can thus be of great assistance for vehicle companies and government departments to develop proper dynamic control algorithms, and to fix roads in a timely manner and at the minimum cost, respectively. As a result, developing easy-to-use and accurate road estimation methods are of great importance in terms of reducing the cost related to vehicles and road maintenance as well as improving passenger comfort and handling capacity. A few books have already been published on road profile modeling and the influence of road unevenness on vehicle response. However, there is still room to discuss road assessment methods based on vehicle response and how road conditions can be used to improve vehicle dynamics. In this book, we use several generalized vehicle models to demonstrate the concepts, methods, and applications of vehicle response-based road estimation algorithms. In addition, necessary tools, algorithms, and methods are illustrated, and the benefits of the road estimation algorithms are evaluated. Furthermore, several case studies of controllable suspension systems to improve vehicle vertical dynamics are presented.
Author: Shuvra Das
Publisher: Springer Nature
ISBN: 3031015088
Category : Technology & Engineering
Languages : en
Pages : 208
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Book Description
Automobiles have played an important role in the shaping of the human civilization for over a century and continue to play a crucial role today. The design, construction, and performance of automobiles have evolved over the years. For many years, there has been a strong shift toward electrification of automobiles. It started with the by-wire systems where more efficient electro-mechanical subsystems started replacing purely mechanical devices, e.g., anti-lock brakes, drive-by-wire, and cruise control. Over the last decade, driven by a strong push for fuel efficiency, pollution reduction, and environmental stewardship, electric and hybrid electric vehicles have become quite popular. In fact, almost all the automobile manufacturers have adopted strategies and launched vehicle models that are electric and/or hybrid. With this shift in technology, employers have growing needs for new talent in areas such as energy storage and battery technology, power electronics, electric motor drives, embedded control systems, and integration of multi-disciplinary systems. To support these needs, universities are adjusting their programs to train students in these new areas of expertise. For electric and hybrid technology to deliver superior performance and efficiency, all sub-systems have to work seamlessly and in unison every time and all the time. To ensure this level of precision and reliability, modeling and simulation play crucial roles during the design and development cycle of electric and hybrid vehicles. Simscape, a Matlab/Simulink toolbox for modeling physical systems, is an ideally suited platform for developing and deploying models for systems and sub-systems that are critical for hybrid and electric vehicles. This text will focus on guiding the reader in the development of models for all critical areas of hybrid and electric vehicles. There are numerous texts on electric and hybrid vehicles in the market right now. A majority of these texts focus on the relevant technology and the physics and engineering of their operation. In contrast, this text focuses on the application of some of the theories in developing models of physical systems that are at the core of hybrid and electric vehicles. Simscape is the tool of choice for the development of these models. Relevant background and appropriate theory are referenced and summarized in the context of model development with significantly more emphasis on the model development procedure and obtaining usable and accurate results.
